# Would nearest point using Geodesic distance and nearest point using Haversine distance be the same point?

I have a point A and trying to find the nearest point to A in a list of points (B, C, D).

I could use knn with `haversine` metrics and get the nearest point like this:

``````knn = NearestNeighbors(n_neighbors=1, metric='haversine')

knn.fit(df['lat', 'lon'])

dist, idx = knn.kneighbors([(35.9157825, -79.0826045)])
``````

However, I'm not sure if this point `df.loc[idx]` will always be the same point i'd get if I calculate distance using geodesic?

knn is very fast compared to having to calculate geodesic distance for all the points in my list. So I would love to use knn if the nearest point would always be the same.

• It should only be different if the geodesic function is pulling from a specified datum... though I am not an expert in this. Commented Jan 31, 2020 at 21:14
• "Always" seems like an impossibly high bar; surely you can design a test case where this fails. Whether this test case is significant with your data is a different question. Commented Jan 31, 2020 at 21:23